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    An approach for physiological motion compensation in robotic-assisted cardiac surgery  Open access

     Aviles Rivero, Angelica Ivone; Sobrevilla Frison, Pilar; Casals Gelpi, Alicia
    Experimental & clinical cardiology
    Vol. 20, num. 22, p. 6713-6724
    Date of publication: 2014-11-14
    Journal article

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    The lack of physiological motion compensation is a major problem in robotic-assisted cardiac surgery. Since the heart is beating while the surgeon carried out the procedure, dexterity of the surgeon¿s and precision are compromised. Due to the operative space and the visibility of the surgical field are reduced, the most practical solution is the use of computer vision techniques. The lack of efficiency and robustness of the existing proposals make physiological motion compensation to be considered an open problem. In this work a novel solution to solve this problem based on the minimization of an energy functional is presented. It is described in the three-dimensional space using the l1-regularized optimization class in which cubic b-splines are used to represent the changes produced on the heart surface. Moreover, the logarithmic barrier function is applied to create an approximation of the total energy in order to avoid its non-differentiability. According to the results, this proposal is able to deal with complex deformations, requires a short computational time and gives a small error.

    The lack of physiological motion compensation is a major problem in robotic-assisted cardiac surgery. Since the heart is beating while the surgeon carried out the procedure, dexterity of the surgeon’s and precision are compromised. Due to the operative space and the visibility of the surgical field are reduced, the most practical solution is the use of computer vision techniques. The lack of efficiency and robustness of the existing proposals make physiological motion compensation to be considered an open problem. In this work a novel solution to solve this problem based on the minimization of an energy functional is presented. It is described in the three-dimensional space using the l1-regularized optimization class in which cubic b-splines are used to represent the changes produced on the heart surface. Moreover, the logarithmic barrier function is applied to create an approximation of the total energy in order to avoid its non-differentiability. According to the results, this proposal is able to deal with complex deformations, requires a short computational time and gives a small error.

  • A recurrent neural network approach for 3d vision-based force estimation

     Aviles Rivero, Angelica Ivone; Marbán González, Arturo; Sobrevilla Frison, Pilar; Casals Gelpi, Alicia; Fernández Ruzafa, Jose
    IEEE International Conference on Image Processing Theory, Tools and Applications
    p. 1-6
    DOI: 10.1109/IPTA.2014.7001941
    Presentation's date: 2014-10-14
    Presentation of work at congresses

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    Robotic-assisted minimally invasive surgery has demonstrated its benefits in comparison with traditional procedures. However, one of the major drawbacks of current robotic system approaches is the lack of force feedback. Apart from space restrictions, the main problems of using force sensors are their high cost and the biocompatibility. In this work a proposal based on Vision Based Force Measurement is presented, in which the deformation mapping of the tissue is obtained using the L2-Regularized Optimization class, and the force is estimated via a recurrent neural network that has as inputs the kinematic variables and the deformation mapping. Moreover, the capability of RNN for predicting time series is used in order to deal with tool occlusions. The highlights of this proposal, according to the results, are: knowledge of material properties are not necessary, there is no need of adding extra sensors and a good trade-off between accuracy and efficiency has been achieved.

    Robotic-assisted minimally invasive surgery has demonstrated its benefits in comparison with traditional procedures. However, one of the major drawbacks of current robotic system approaches is the lack of force feedback. Apart from space restrictions, the main problems of using force sensors are their high cost and the biocompatibility. In this work a proposal based on Vision Based Force Measurement is presented, in which the deformation mapping of the tissue is obtained using the L2-Regularized Optimization class, and the force is estimated via a recurrent neural network that has as inputs the kinematic variables and the deformation mapping. Moreover, the capability of RNN for predicting time series is used in order to deal with tool occlusions. The highlights of this proposal, according to the results, are: knowledge of material properties are not necessary, there is no need of adding extra sensors and a good trade-off between accuracy and efficiency has been achieved.

  • Unconstrained L1-regularized minimization with interpolated transformations for heart motion compensation

     Aviles Rivero, Angelica Ivone; Sobrevilla Frison, Pilar; Casals Gelpi, Alicia
    IEEE Engineering in Medicine and Biology Society
    p. 5109-5112
    DOI: 10.1109/EMBC.2014.6944774
    Presentation's date: 2014-08-29
    Presentation of work at congresses

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    Motion compensation constitutes a challenging issue in minimally invasive beating heart surgery. Since the zone to be repaired has a dynamic behaviour, precision and surgeon's dexterity decrease. In order to solve this problem, various proposals have been presented using l2-norm. However, as they present some limitations in terms of robustness and efficiency, motion compensation is still considered an open problem. In this work, a solution based on the class of l1-Regularized Optimization is proposed. It has been selected due to its mathematical properties and practical benefits. In particular, deformation is characterized by cubic B-splines since they offer an excellent balance between computational cost and accuracy. Moreover, due to the non-differentiability of the functional, the logarithmic barrier function is used for generating a standard optimization problem. Results have provided a very good trade-off between accuracy and efficiency, indicating the potential of the proposed approach and proving its stability even under complex deformations.

  • State of the art survey on MRI brain tumor segmentation

     Gordillo Castillo, Nelly; Montseny Masip, Eduard; Sobrevilla Frison, Pilar
    Magnetic resonance imaging
    DOI: 10.1016/j.mri.2013.05.002
    Date of publication: 2013-06-20
    Journal article

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    Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.

    Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema,and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.

  • Robustness analysis of the reduced fuzzy texture spectrum and its performance on noisy images

     Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Barcelo, Aina
    Advances in imaging and electron physics
    Vol. 179, p. 263-290
    DOI: 10.1016/B978-0-12-407700-3.00004-1
    Date of publication: 2013
    Journal article

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    In a previous study, we introduced the fuzzy texture spectrum (FTS) as a texture spectrum (TS)-based method, where a given texture was characterized by its associated spectrum using fuzzy techniques. It allowed for the definition of a spectrum that was more likely to be visible to human perception. Afterward, with the aim of improving the computational efficiency of the FTS, we modified it by grouping in the same class all the texture units differing by rotations of 45 degrees. In the original TS encoding, it was suggested to use a 20 × 20 pixels window to characterize the texture of an image through the TS. We present a set of experiments for determining the size that is best suited for characterizing natural images using a database of Brodatz images. A second set of experiments is presented for analyzing the performance of the texture and fuzzy texture encodings against noise. Finally, we study the ability of both encodings to identify Brodatz images belonging to the same class. To carry out the experiments, we use information theory and similarity and dissimilarity measures. The results obtained have proven that, in general, fuzzy encoding outperforms the performance of the original encoding

  • Variability estimation of hue and saturation components in the HSV space

     Romaní Also, Santiago; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    Color research and application
    Vol. 37, num. 4, p. 261-271
    DOI: 10.1002/col.20699
    Date of publication: 2012-08
    Journal article

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  • Detección de Lesiones Pequeñas de Esclerosis Múltiple en Imágenes de Resonancia Magnética mediante la Aplicación de Técnicas Difusas

     Aymerich Martínez, Francisco Javier
    Department of Automatic Control, Universitat Politècnica de Catalunya
    Theses

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  • Fuzzy approach toward reducing false positives in the detection of small multiple sclerosis lesions in magnetic resonance images

     Aymerich Martínez, Francisco Javier; Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Rovira Cañellas, Alex
    IEEE Engineering in Medicine and Biology Society
    p. 5694-5697
    DOI: 10.1109/IEMBS.2011.6091378
    Presentation's date: 2011-09-02
    Presentation of work at congresses

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  • Detection of hyperintense regions on MR brain images using a mamdani type fuzzy rule-based system

     Aymerich Martínez, Francisco Javier; Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Rovira, Alex
    IEEE International Conference on Fuzzy Systems
    p. 751-758
    DOI: 10.1109/FUZZY.2011.6007737
    Presentation's date: 2011-06
    Presentation of work at congresses

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  • Decision tree based fuzzy classifier of 1H magnetic resonance spectra from cerebrospinal fluid samples

     Aymerich Martínez, Francisco Javier; Alonso, J.; Comabella, Manuel; Cabañas, M. E.; Sobrevilla Frison, Pilar; Rovira, A.
    Fuzzy sets and systems
    Vol. 170, num. 1, p. 43-63
    DOI: 10.1016/j.fss.2011.01.003
    Date of publication: 2011-05-01
    Journal article

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  • Fuzzy-based analysis of microscopic color cervical pap smear images: nuclei detection

     Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Vaschetto, Favio; Lerma, Enrique
    International journal of computational intelligence and applications
    Vol. 9, num. 3, p. 187-206
    DOI: 10.1142/S1469026810002860
    Date of publication: 2010-09
    Journal article

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  • Contributions to Automatic and Unsupervised MRI Brain Tumor Segmentation: A New Fuzzy Approach  Open access

     Gordillo Castillo, Nelly
    Department of Automatic Control, Universitat Politècnica de Catalunya
    Theses

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    Brain tumors are part of a group of common, non-communicable, chronic and potentially lethal diseases affecting mostfamilies in Europe. Imaging plays a central role in brain tumor management, from detection and classification to staging andcomparison. Increasingly, magnetic resonance imaging (MRI) scan is being used for suspected brain tumors, because in addition tooutline the normal brain structures in great detail, has a high sensitivity for detecting the presence of, or changes within, a tumor.Currently most of the process related to brain tumors such as diagnosis, therapy, and surgery planning are based on its previoussegmentation from MRI. Brain tumor segmentation from MRI is a challenging task that involves various disciplines. The tumors to besegmented are anatomical structures, which are often non-rigid and complex in shape, vary greatly in size and position, and exhibitconsiderable variability from patient to patient. Moreover, the task of labeling brain tumors in MRI is highly time consuming and thereexists significant variation between the labels produced by different experts. The challenges associated with automated brain tumor segmentation have given rise to many different segmentationapproaches. Although the reported accuracy of the proposed methods is promising, these approaches have not gained wide acceptance among the neuroscientists for every day clinical practice. Two of the principal reasons are the lack of standardizedprocedures, and the deficiency of the existing methods to assist medical decision following a technician way of work. For a brain tumor segmentation system has acceptance among neuroscientists in clinical practice, it should supportmedical decision in a transparent and interpretable way emulating the role of a technician, considering his experience and knowledge. This includes knowledge of the expected appearance, location, variability of normal anatomy, bilateral symmetry, andknowledge about the expected intensities of different tissues. The image related problems and the variability in tissue distribution among individuals in the human population makes that some degree of uncertainty must be considered together with segmentationresults. A possible solution for designing complex systems, in which it is required to incorporate the experience of an expert, or the related concepts appear uncertain, is the use of soft computing techniques such as fuzzy systems. An important advantage of fuzzysystems is their ability for handling vague information. In this work, it is proposed the development of a method to assist the specialists in the process of segmenting braintumors. The main objective is to develop a system that can follow a technician way of work, considering his experience andknowledge. More concretely, it is presented a fully automatic and unsupervised segmentation method, which considers humanknowledge. The method successfully manages the ambiguity of MR image features being capable of describing knowledge about thetumors in vague terms. The method was developed making use of the powerful tools provided by fuzzy set theory. This thesis presents a step-by-step methodology for the automatic MRI brain tumor segmentation. For achieving the fullyautomatic and unsupervised segmentation, objective measures are delineated by means of adaptive histogram thresholds for defining the non-tumor and tumor populations. For defining the tumor population a symmetry analysis is conducted. The proposed approach introduces a new way to automatically define the membership functions from the histogram. The proposed membership functions are designed to adapt well to the MRI data and efficiently separate the populations. Since any post-processing is needed, and the unique pre-processing operation is the skull stripping, the proposed segmentation technique reduces the computational times. The proposed approach is quantitatively comparable to the most accurate existing methods, even thoughthe segmentation is done in 2D.

  • A new fuzzy approach to brain tumor segmentation

     Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Gordillo Castillo, Nelly
    IEEE International Conference on Fuzzy Systems
    p. 325-332
    Presentation's date: 2010-07-20
    Presentation of work at congresses

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  • Filtering False Detections of Small Multiple Sclerosis Lesions using Fuzzy Regional Analysis

     Aymerich Martínez, Francisco Javier; Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Rovira, Alex
    IEEE International Conference on Fuzzy Systems
    p. 1471-1478
    Presentation's date: 2010-07-20
    Presentation of work at congresses

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    A Fuzzy Regional-Based Approach for Detecting Cerebrospinal Fluid Regions in presence of Multiple Sclerosis Lesions  Open access

     Aymerich Martínez, Francisco Javier; Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Rovira, Alex
    International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
    p. 552-561
    Presentation's date: 2010-06-30
    Presentation of work at congresses

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  • Cytoplasm contour approximation based on color fuzzy sets and color gradient

     Romani, Santiago; Prados-Suárez, María Belén; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
    p. 645-654
    DOI: 10.1007/978-3-642-14049-5_66
    Presentation's date: 2010-06-29
    Presentation of work at congresses

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  • Caracterización del color del citoplasma en imágenes de citología cervico-vaginal

     Romaní Also, Santiago; Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Lerma, Enrique
    Congreso Español sobre Tecnologías y Lógica Fuzzy
    p. 343-348
    Presentation's date: 2010-02-03
    Presentation of work at congresses

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  • Preface

     Sobrevilla Frison, Pilar; Aranda López, Juan; Xambó Descamps, Sebastian
    Date of publication: 2010
    Book chapter

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  • THREECOND: an automated and unsupervised three colour Fuzzy-based algorithm for detecting nuclei in cervical pap smear images

     Vaschetto, Favio; Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Lerma, Enrique
    International Conference on Intelligent Systems Design and Applications
    p. 1359-1364
    DOI: 10.1109/ISDA.2009.212
    Presentation's date: 2009-11-30
    Presentation of work at congresses

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  • On quality assessment of corneal endothelium and its possibility to be used for surgical corneal transplantation

     Tiñena Salvaña, Francesc; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    IEEE International Conference on Fuzzy Systems
    p. 1326-1331
    DOI: 10.1109/FUZZY.2009.5277395
    Presentation's date: 2009-08-22
    Presentation of work at congresses

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  • On the reliability of the color gradient vector argument approach

     Prados-Suárez, María Belén; Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Romaní, Santiago
    International Fuzzy Systems Association World Congress and European Society of Fuzzy Logic and Technology Conference
    p. 1863-1868
    Presentation's date: 2009-07-20
    Presentation of work at congresses

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  • FLCSFD - A fuzzy local-based approach for detecting cerebrospinal fluid regions in presence of MS lesions

     Aymerich Martínez, Francisco Javier; Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Rovira, Alex
    IEEE/ICME International Conference on Complex Medical
    p. 1-6
    DOI: 10.1109/ICCME.2009.4906622
    Presentation's date: 2009-04-09
    Presentation of work at congresses

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    Magnetic Resonance Imaging (MRI) is an important paraclinical tool for diagnosing Multiple Sclerosis (MS) and providing several markers of disease activity and evolution. Traditionally, hypointense lesions on T1-weighted images have been reported to represent areas where demyelination and axonal loss have occurred, and are the images usually selected for segmenting the encephalic parenchyma. Based on the fact that in T1-weighted images MS lesions cannot be located within cerebrospinal fluid regions (CSF), a correct detection of such regions is very useful to filter MS’s false detections. However, the gray levels similarity among some MS lesions and CDF regions makes of the encephalic parenchyma detection process a difficult task. In this work we propose an approach for detecting CSF regions in which, for taking into consideration aforementioned gray-level vagueness, as well as the intrinsic uncertainty of CSF boundaries, we make use of fuzzy techniques. The proposed algorithm performs a fuzzy local analysis based on gray-level and texture characteristics, but considering the location and size of the CSF regions. As a result, the algorithm allows discriminating cerebrospinal fluid regions inside the intracranial region, providing confidence degrees that match with the possibility of including pixels associated to MS lesion

  • FLCSFD - A fuzzy local-based approach for detecting cerebrospinal fluid (CSF) regions in presence of MS lesions

     Aymerich Martínez, Francisco Javier; Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Rovira, Alex
    International Conference on Complex Medical Engineering
    p. 1-6
    DOI: 10.1109/ICCME.2009.4906622
    Presentation's date: 2009-04-09
    Presentation of work at congresses

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  • A new fuzzy-based system for extracting structural features allowing to capture stripes' image local patterns

     Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Grau Sanchez, Miguel
    IEEE International Systems Conference
    p. 389-394
    Presentation's date: 2009-03-25
    Presentation of work at congresses

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  • A Fuzzy-based Automated Cells Detection System for Color Pap Smear Tests ¿-FACSDS¿

     Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Lerma, Enrique
    Date of publication: 2008-10
    Book chapter

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    Estudi de la transformació de l'espai de color RGB a l'espai de color HSV  Open access

     Grau Gotés, Mª Ángela; Grau Sanchez, Miguel; Montseny Masip, Eduard; Sobrevilla Frison, Pilar
    Date: 2008-09-26
    Report

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    S’apliquen les tècniques clàssiques de propagació de l’error a la transformació de l’espai de color RGB en l’espai de color HSV a un conjunt de 1098 imatges test. El conjunt d’imatges test són 183 paletes de color i sis nivells d’il·luminació diferents. Els resultats que es presenten indiquen com varien la mitjana i la variància per la transformació.

  • Structured texture detection through fuzzy texture spectrum analysis

     Grau Sanchez, Miguel; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    International Conference Information Processing and Management of Uncertainty in Knowledge-Based Systems
    p. 1183-1190
    Presentation's date: 2008-06-25
    Presentation of work at congresses

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  • SISTEMA DE VISION POR COMPUTADOR PARA EL CRIBADO AUTOMATICO DE CITOLOGIAS CERVICO-VAGINALS BASADO EN SOFTCOMPUTING

     Sobrevilla Frison, Pilar; Montseny Masip, Eduard; Tiñena Salvaña, Francesc; Grau Gotés, Mª Ángela; Trias Pairo, Juan; Grau Sanchez, Miguel; Xambó Descamps, Sebastian; Mares Marti, Pere; Gordillo Castillo, Nelly; Romaní Also, Santiago
    Competitive project

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  • Image Processing. Fuzzy sets and systems

     Sobrevilla Frison, Pilar
    Vol. 158, num. 3
    Collaboration in journals

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  • Fuzzy Texture Unit and Fuzzy Texture Spectrum for texture characterization

     Barcelo, A; Montseny Masip, Eduard; Sobrevilla Frison, Pilar
    Fuzzy sets and systems
    Vol. 158, num. 3, p. 239-252
    Date of publication: 2007-02
    Journal article

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  • Special issue: Image processing - Editorial

     Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    Fuzzy sets and systems
    Vol. 158, num. 3, p. 213-214
    Date of publication: 2007-02
    Journal article

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  • Comunication module Lancelan

     Ros Florenza, Joaquin; Sobrevilla Frison, Pilar
    International Conference ICBL
    Presentation of work at congresses

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  • On the Use of Fuzzy Texture Spectrum for Homogeneous and Textured Image Discrimination

     Grau Sanchez, Miguel; Montseny Masip, Eduard; Sobrevilla Frison, Pilar
    2007 IEEE Conference on Fuzzy Systems
    p. 1886-1891
    Presentation of work at congresses

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  • On the use of the distribution function for generating fuzzy membership functions

     Montseny Masip, Eduard; Sobrevilla Frison, Pilar
    II Congreso Español de Informática
    p. 229-236
    Presentation of work at congresses

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  • An approach to manage Hue and Saturation uncertainty addressed to colour segmentation algorithms

     Romaní, S; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    International journal of intelligent systems technologies and applications
    Vol. 1, num. 3/4, p. 393-408
    Date of publication: 2006-06
    Journal article

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  • Labeled Color Image Segmentation through Perceptually Relevant Chromatic Patterns

     ROMANI ALSO, SANTIAGO
    Department of Computer Science, Universitat Politècnica de Catalunya
    Theses

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  • Towards automatic image texture characterization through no-supervised fuzzy texture spectrum

     Barcelo, A; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    Proceedings of the 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
    p. 1518-1524
    Presentation of work at congresses

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  • Fuzzy Texture Spectrum vs Texture Spectrum a Comparative Study

     Barceló, A; Montseny Masip, Eduard; Sobrevilla Frison, Pilar
    International journal of approximate reasoning
    p. 1
    Date of publication: 2006-01
    Journal article

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  • Fuzzy-based automatic approach for underwater docks` anomalies detection

     Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Romani, S; Monferrer De La Peña, Alexandre
    2006 Annual Meeting of the North American Fuzzy Information Processing Society
    p. 438-443
    Presentation of work at congresses

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  • Robustness and performance evaluationof the fuzzy texture spectrum encoding

     Barcelo, A; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    2006 IEEE World Congress on Computational Intelligence,The 15th IEEE International Conference on Fuzzy Systems
    p. 6629-6636
    Presentation of work at congresses

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  • On automatic underwater piers walls anomalies detection using color images

     Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Romani, S; Monferrer De La Peña, Alexandre
    International Conference on Maritime Transport
    p. 671-677
    Presentation of work at congresses

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  • Nuevas Técnicas de SoftComputing

     Sobrevilla Frison, Pilar
    Segundas Jornadas Internacionales de Inteligencia Artificial. Visión por Computador
    Presentation's date: 2005-12-01
    Presentation of work at congresses

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  • Merging pixels' location and illumination levels information for getting automatic fuzzy perceptual image segmentation algorithms

     Grau Gotés, Mª Ángela; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    Date of publication: 2005-06
    Book chapter

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  • An approach to Color Gradient Vector Argument considering Data Uncertainty

     Prados-Suárez, María Belén; Montseny Masip, Eduard; Sobrevilla Frison, Pilar; Romaní, S
    4th Conference for Fuzzy Logic and Technology and 11 Rencontres Pharancophones sur la Logique Floue et ses Applications
    p. 705-708
    Presentation of work at congresses

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  • Merging Pixels' Location and Illumination Levels Information for getting Automatic Fuzzy Perceptual Image Segmentation Algorithms

     Grau Gotés, Mª Ángela; Sobrevilla Frison, Pilar; Montseny Masip, Eduard
    2005 Annual Meeting of the North American Fuzzy Information Processing Society
    p. 367-372
    Presentation of work at congresses

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